AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-Li Huang, Ph.D. Assistant Professor Industrial Engineering Department New Mexico State University 575-646-2950 yhuang@nmsu.edu 1
Learning Objectives 1. Understand the fundamental reasons and shortcomings of the current scheduling systems. 2. Define an effective patient central scheduling model that meets the clinic policies. 3. Develop and extend the proposed model considering ancillary services. 4. Learn and evaluate the implementation of the proposed model. 5. Develop a overbooking policy to reduce the negative impact of no-shows. 2
Introduction Shift inpatient care to outpatient facilities to reduce cost Competition increases Currently focus more on the efficiency of provider time Lack of implemented model 3
Learning Objective 1 Unrealistic Estimations of Treatment Time Physician centric solution to the problem of wait time Underestimating visit times Overbooking or double booking Physician s perception Patient Arrival Time vs. Patient Appointment Time Often blurred in practice Physician idling may compounds patient wait time 4
Learning Objective 1 Impact of Inaccurate Estimated of Treatment Time Even One Minute on average will make a significant difference. 5
Learning Objective 1 Literature Limitation Most studies are either theoretical or case-specific. Appointment rules have not been adopted and implemented successfully in reality due to the lack of understanding and cooperation of the medical staff. No real case study supports these appointment rules. 6
Learning Objective 1 Literature Limitation The variability of the physician treatment time has not been addressed from the patients standpoint. Major Problem min. W c + P W c P (cost ratio) The probability of a patient delay at any given time has never been studied to the point that the patients of each type should be treated equally at any given time in the pre-determined template (slots). There isn t a clear distinguish between the physician s schedule and the patients schedule. 7
Appointment Rules
General Modeling Definitions and Formulations Ti = Physician s service time to treat patient i where i = 1, 2, 3,, 2 ~ D μ, σ T i n, and ( ) n = The number of patients scheduled per session X = The scheduled time interval for a patient in minutes S i = The scheduled time to start patient i where i = 1, 2, 3,, n, and Si = Si 1 + X = ( i 1)X S 1 = 0 F i = The finish time for patient i where i = 1, 2, 3,, n 9
10 General Modeling Definitions and Formulations = i A The actual time to start patient i where i = 1, 2, 3,, n = i W The wait time for patient i in minutes where i = 1, 2, 3,, n i i i T A F + = Otherwise S F if F S A i i i i i < = 1 1 i i i S A W = = i P The physician idle time waiting for patient i where i = 1, 2, 3,, n 1 = i i i F A P
General Modeling Definitions and Formulations Physician Service Time T 1 T 2 T 3 T 4 T 5 Patient Scheduled Time 11
General Modeling Definitions and Formulations W = P = The average patient wait time W n i= = 1 W n The average physician idle time i P n i= = 1 n P i 12
Learning Objective 2 Wait Ratio d = Number of standard deviation away from μ X = μ + dσ R = The wait ratio, which is the degree to which patient wait time exceeds R physician idle time W P = R P R = W d * X * = μ + d * σ 13
Learning Objective 2 Wait Ratio Example: T i ( μ = 15, σ 4) ~ Gamma = for Return Visit patients, a simulation run. n = 32 R = 3, 32 patients a day, Wait Ratio is equal to 3 14
Learning Objective 2 Wait Ratio Example 15
Learning Objective 2 Wait Ratio Example The simulation found d * = 0.63. Therefore, the is calculated as: * * X = μ + d σ = 15 + 0.63 4 = 17.5 min X * 16
Learning Objective 2 Determine the best wait ratio - Underlying Constraints Let X be the Scheduled Time Interval, the i th patient wait time in general is: W i consists of W and i 1 i 1 ( T X ) In order not to generate additional waiting, the 1 term 2 is preferred to be less than 0. Therefore, given T i ~ D μ, σ and best time interval * X, we can only hope: Pr ( ) X * 0. 5 T i T i 2 (, ) ~ D μ σ ( T i X ) ( ) X * 17
Learning Objective 2 Determine the best wait ratio - Clinical Constraints Clinic or session finish time Time of last appointment Number of patients to be seen in a given session Average patient wait time Maximum patient wait time Average physician Idle time Maximum physician Idle time 18
Learning Objective 2 Example Orthopedic Surgery Clinic Patient Type Average Standard Deviation NP 10.6 4.5 FU 7.3 4.0 XR 5.5 3.4 NP: new patients FU: follow-up patients XR: patients need x-ray before being seen Unit: minutes 19
Learning Objective 2 Orthopedic Surgery Clinic 20
Learning Objective 2 Example Orthopedic Surgery Clinic Clinic constraints: Session finished by 11:30 a.m. (210 min) Last patient scheduled by 11:00 a.m. (180 min) 25 patients in a session 21
Learning Objective 2 Example Orthopedic Surgery Clinic 22
Learning Objective 2 Example Orthopedic Surgery Clinic The wait ratio that satisfies both constraints is 18:1, which Pr ( ) X * 57% T i 23
Learning Objective 2 Provider Schedule Orthopedic Surgery Clinic Provider Schedule: Once best treatment time intervals for each visit type are determined, the schedule can be set up accordingly. For example: assuming start time is at 8:00 a.m. Visit Type Best Time Interval (min.) FU 7.4 NP 10.6 XR 5.3 Visit Type M Physician Schedule FU 8:00 XR 8:07 NP 8:13 FU 8:23 XR 8:31 NP 8:36 M 24
Learning Objective 3 Patient Arrival Schedule Patient Arrival Schedule: Once the provider schedule is set up, then patients arrival is scheduled accordingly. The determination of Enough Time includes: Signing in Filling out paperwork Having vital or x-ray taken Moving from room to room Being seen by Medical Assistance or Nurse Providing a specimen.. Goal is to minimize average patient wait time. 25
Learning Objective 3 Patient Arrival Schedule Orthopedic Surgery Clinic X-ray is the main ancillary service that is focused on. 70 60 50 Histogram of x-ray Gamma α = 3.32 β = 2.11 Frequency 40 30 20 10 0 0 4 8 12 x-ray 16 20 24 28 26
Learning Objective 3 Patient Arrival Schedule Orthopedic Surgery Clinic Let Y be the scheduled time interval of ancillary service X ray ~ Gamma ( 2 ) ( * ) * 6.3,3.7 Pr X ray Y = 0.35 Y = 5 min. 27
Learning Objective 3 Patient Arrival Schedule Orthopedic Surgery Clinic 10 minutes for all pre-visit activities and 5 minutes for taking x-ray for XR patients. (Arrival times are rounded to the nearest 5-minute increment) Visit Type M Physician Schedule M Patient Arrival Schedule FU 8:00 7:50 (0 10) XR 8:07 7:50 (7.4-15) NP 8:13 8:00 (12.7-10) FU 8:23 8:15 (23.3 10) XR 8:31 8:15 (30.7 15) NP 8:36 8:25 (36 10) M 28
Learning Objective 3 Patient Arrival Schedule Orthopedic Surgery Clinic 23% of NP and 21% of FU needs X-ray before seeing physician and are most likely determined by RN or MA. Existing Rules: Patients who have had joint replacement will need to have x-rays at post operation, 3 month check, and 1year check scheduled under FU patient slots. After a year, if a patient call in and complain of pain, they would be scheduled as FU and have an x-ray. Patients who have had bone displacement or fracture that was manipulated or operated on in the hospital will need an x-ray and are scheduled as FU. 29
Learning Objective 3 Patient Arrival Schedule Orthopedic Surgery Clinic Existing Rules: All new patients will need x-rays if they have not had one done elsewhere. A special case is new patients with an indication of arthritis in their knee; if their x-ray only has two views, then they will need to have x-ray for two additional views. Patients who are 60+ years old will need x-rays. 30
Learning Objective 4 Case Study General Problems Patient wait time is very sensitive to the average treatment time yet most clinics collects no data on average treatment times. Clinics usually have 15 or 30 minute slots regardless the difference on the practice of individual physician which generates additional wait time either on the patients or the physician. 31
Learning Objective 4 Case Study General Problems There isn t a clear distinguish between physician schedule and patient arrival schedule. No ancillary services being considered. Many clinic schedules are designed to overbook appointments to prevent idle time for physician, which creates unnecessary patient wait time. 32
Learning Objective 4 Case Study Orthopedic Surgery Clinic 33
Learning Objective 4 Case Study Summary Case 1 (Orthopedic Surgery): Current Proposed Visit Scheduled Wait Scheduled Wait Type Time (min) Ratio Time (min) Ratio NP 10 58:1 10.6 18:1 FU 5 :1 7.4 18:1 XR none :1 5.3 18:1 Results: Avg. Patient Wait Time (min) Est. Avg. Physician Idle Time (min) Before 27.8 0.2 After 13.1 0.8 % Reduction 53% 34
Learning Objective 4 Case Study Summary Case 2 (Plastic Surgery): Current Proposed Visit Type Scheduled Time (min) Wait Ratio Scheduled Time (min) Wait Ratio NP 30 25:1 33.3 3:1 RV 15 33:1 17.5 3:1 POP 15 53:1 17.4 3:1 HP 15 :1 20.4 3:1 Results: Avg. Patient Wait Time (min) Est. Avg. Physician Idle Time (min) Before 15.0 5.4 After 7.5 5.3 % Reduction 50% 35
Learning Objective 4 Case Study Summary Case 3 (Vascular Surgery): Current Proposed Visit Type Scheduled Time (min) Wait Ratio Scheduled Time (min) Wait Ratio NP 30 1:9 20.7 9:1 RV 15 6:1 14.6 9:1 Results: Avg. Patient Wait Time (min) Est. Avg. Physician Idle Time (min) Before 27.8 2.0 After 12.4 1.1 % Reduction 56% 36
Learning Objective 4 Case Study Summary Private Clinic Schedule by Physician Case Study 1 Scenarios Teaching Clinic Single Resident Schedule by Resident Schedule by Physician Case Study 2 Multiple Residents Schedule by Physician Case Study 3 37
Learning Objective 5 Introduction to No-show Some of the negative consequences of patient no show are: They create disturbances in the system Reduced provider productivity and clinic efficiency Increased healthcare costs Limited patient access to care 38
Learning Objective 5 Introduction to No-show Issue with Open Access Doing today s work today Burden patients in attempting to get appointments Patients and profit loss Allow overtime to complete High patient wait time 39
Learning Objective 5 Proposed Overbooking Policy Basic steps: 1. Use a predictive no-show model to estimate individual no-show probabilities 2. Find the optimal no-show threshold that minimizes the total costs (patient wait time, physician idle time, and overtime) 3. Overbook slots where the patient s no-show probability is greater than the threshold Slots can be overbooked more than once if the combined no-show rate of patients in any given slot exceeds the threshold 40
Learning Objective 5 Proposed Overbooking Policy 41
Learning Objective 5 Proposed Overbooking Policy p, Given p* = 0.28, i j is the predicted no-show rate for patient j at slot i i Scheduled time slots j=1 First booked patient 1=Yes 2=No p i,1 j=2 first overbooked patient if p i,1 p* p i,2 p i,1 x p i,2 j=3 second overbooked patient if p i,1 x p i,2 p* Total overbooked (j=2) +( j=3) Total booked 1 8:00 1 0.14 0 1 2 8:15 1 0.37 1 0.38 0.14 0 1 2 3 8:30 1 0.21 0 1 4 8:45 1 0.55 1 0.42 0.21 0 1 2 5 9:00 1 0.22 0 1 6 9:15 1 0.37 1 0.16 0.06 0 1 2 7 9:30 1 0.59 1 0.38 0.22 0 1 2 8 9:45 1 0.21 0 1 9 10:00 1 0.24 0 1 10 10:15 1 0.36 1 0.32 0.12 0 1 2 42
Implementation Steps and Outcomes Overview Step 1: Activities Development Outcomes Data for physician and medical staff treatment time Data for patient and physician wait time Understand patient visit time and its variability Improve physician and staff understanding of time required for different patient types 43
Implementation Steps and Outcomes Overview Step 2: Activities Development Outcomes Clinic process flow Clinic constraints Build simulation model corresponding to the process flow of the clinic Determine the best treatment time interval for each visit type Build physician schedule Develop improved scheduling policies for providers 44
Implementation Steps and Outcomes Overview Step 3: Activities Development Outcomes Date for treatment time of ancillary services Build patient arrival schedule Develop improved scheduling practices for patient arrival 45
Implementation Steps and Outcomes Overview Step 4: Activities Development Outcomes Data for no-show patient characteristics and preferences Build statistical model to estimate no-show rate Incorporate overbooking policy in scheduling system Improve patient access to care providers and reduce negative impact of no-shows 46
Implementation Steps and Outcomes Overview Step 5: Activities Development Outcomes Date for patient and physician wait time after implementation Approach implementation in clinics Enhance patient satisfaction and quality of care 47
Conclusion Contributions Present the impact where the scheduled treatment times are not based on the actual data. Understand the benefit and difference of wait ratio concept to the traditional cost ratio. Provide step-by-step approach to develop physician and patient arrival schedules. Implement the proposed solution for three clinics to further demonstrate the effectiveness and the simplicity of the approach. 48
5. Conclusion Contributions A cost-effective overbooking policy that accounts for individual patient s no-show rate. Change patients perception of long wait in a physician office. Provide a better quality service in terms of patients waiting. Create a much less stressful working environment for the medical staff. 49
Questions? Please contact 50